Home | Data Overview | Multi-Dimensional Analysis | Conclusion

Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care

Function

To begin, let’s explore the function we created to run all of this statistical analysis:

mysubsetMDS <- function(x){
  mysubset <- df %>% 
    select(starts_with(x))
  
  meta <- metaMDS(mysubset)
  MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
    cbind(demo)
  return(MDS_df)
}


In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring Gender.

Question Types

Science Identity

Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:

This is great and all, but let’s run an adonis test to see if there is a significant difference in how different career goals responded to science identity questions:

## 
## Call:
## adonis(formula = si ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)    
## demo$career   8    1.1150 0.139375  4.7908 0.11229  0.001 ***
## Residuals   303    8.8149 0.029092         0.88771           
## Total       311    9.9299                  1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This shows that the differences in answers are not significant, according to career goals.

Career Motivation

Next, let’s look into Carer Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = cm ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.5407 0.067592  1.3393 0.03415  0.188
## Residuals   303   15.2921 0.050469         0.96585       
## Total       311   15.8329                  1.00000

This shows that the differences in answers are not significant, according to career goals.


Intrinsic Motivation

Now, Intrinsic Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = im ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.2429 0.030358  1.0734 0.02756  0.334
## Residuals   303    8.5690 0.028281         0.97244       
## Total       311    8.8119                  1.00000

This shows that the differences in answers are not significant, according to career goals.

Self-Determination

Now, Self-Determination:

And an Adonis test:

## 
## Call:
## adonis(formula = sd ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.2123 0.026543  1.0862 0.02788  0.353
## Residuals   303    7.4045 0.024437         0.97212       
## Total       311    7.6168                  1.00000

This shows that the differences in answers are not significant, according to career goals.

Self-Efficacy

Now, Self-Efficacy:

And an Adonis test:

## 
## Call:
## adonis(formula = se ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.2625 0.032817  1.2062 0.03087  0.282
## Residuals   303    8.2434 0.027206         0.96913       
## Total       311    8.5059                  1.00000

This shows that the differences in answers are not significant, according to career goals.

Grade Motivation

Now, Grade Motivation:

And an Adonis test:

## 
## Call:
## adonis(formula = gm ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.1076 0.013446 0.60059 0.01561  0.808
## Residuals   303    6.7833 0.022387         0.98439       
## Total       311    6.8909                  1.00000

This shows that the differences in answers are not significant, according to career goals.

Competency in Science

Now, Competency in Science:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comp ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8     0.291 0.036377  1.0484 0.02694  0.394
## Residuals   303    10.513 0.034696         0.97306       
## Total       311    10.804                  1.00000

Look at that! The differences are significant for Science Competency across career goals.

Personal Community Orientation

Now, Personal Community Orientation:

And an Adonis test:

## 
## Call:
## adonis(formula = per_comm_orient ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs   MeanSqs F.Model      R2 Pr(>F)   
## demo$career   8   0.22325 0.0279061  3.3536 0.08134  0.007 **
## Residuals   303   2.52133 0.0083212         0.91866          
## Total       311   2.74458                   1.00000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

This shows that the differences in answers are not significant, according to career goals.

Science Community Orientation

Now, Competency in Science:

Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:

And an Adonis test:

## 
## Call:
## adonis(formula = sci_comm_orient ~ demo$career) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##              Df SumsOfSqs  MeanSqs F.Model      R2 Pr(>F)
## demo$career   8    0.1212 0.015151  1.0266 0.02639  0.403
## Residuals   303    4.4717 0.014758         0.97361       
## Total       311    4.5929                  1.00000

This shows that the differences in answers are not significant, according to major.

Conclusion

Ultimately, what we can understand from all of this is that students significantly answer Science Identity and Personal Communal Orientation questions differently according to their career goals.

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